{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.864\n",
      "3.92\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import json\n",
    "from utils import *\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体字体\n",
    "\n",
    "random.seed(42)\n",
    "\n",
    "with open(\"trees.json\", 'r') as json_file:\n",
    "    trees = json.load(json_file)\n",
    "\n",
    "# test_input = trees[str(0)]\n",
    "\n",
    "# test_input = {int(key): value for key, value in test_input.items()}\n",
    "\n",
    "# nodes = [i + 1 for i in range(len(test_input.keys()))]\n",
    "\n",
    "node_nums = [len(tree) for tree in trees.values()]\n",
    "tree_depths = [max([len(chain) for chain in tree.values()]) for tree in trees.values()]\n",
    "\n",
    "avg_num_nodes = sum(node_nums) / len(node_nums)\n",
    "avg_tree_depth = sum(tree_depths) / len(tree_depths)\n",
    "print(avg_num_nodes)\n",
    "print(avg_tree_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [02:45<00:00,  3.03it/s]\n"
     ]
    }
   ],
   "source": [
    "data = pd.DataFrame(columns=[\"num_nodes\", \"method\", \"explore_times\"])\n",
    "for key in tqdm(trees.keys()):\n",
    "    test_input = {int(key): value for key, value in trees[key].items()}\n",
    "    nodes = [i + 1 for i in range(len(test_input.keys()))]\n",
    "    num_nodes = len(test_input)\n",
    "    explore_times = 0\n",
    "    explore_times_2 = 0\n",
    "    repeat_times = 5\n",
    "    for k in range(repeat_times):\n",
    "        known_chains = {}\n",
    "        known_nodes = set()\n",
    "        depths = {i: [d + 1 for d in range(len(nodes))] for i in nodes}\n",
    "        parents = {i: None for i in nodes}\n",
    "        for _ in range(len(nodes)):\n",
    "            explore_times += 1\n",
    "            # way 2: 推理+不探索已知节点\n",
    "            nodes_to_select = list(set(nodes).difference(set(known_chains.keys()).union(set(known_nodes))))\n",
    "            selected_node = nodes_to_select[random.randint(0, len(nodes_to_select)-1)]\n",
    "            known_chains[selected_node] = test_input[selected_node]\n",
    "            update(known_chains, nodes, depths, parents)\n",
    "            for node in nodes:\n",
    "                if len(depths[node]) == 1:\n",
    "                    known_nodes.add(node)\n",
    "            if len(nodes) - 1 == len([i for i in parents.keys() if parents[i] is not None]):\n",
    "                break\n",
    "    for k in range(repeat_times):\n",
    "        known_chains = {}\n",
    "        known_nodes = set()\n",
    "        depths = {i: [d + 1 for d in range(len(nodes))] for i in nodes}\n",
    "        parents = {i: None for i in nodes}\n",
    "        for _ in range(len(nodes)):\n",
    "            explore_times_2 += 1\n",
    "            # way 3: 推理+不探索已知节点+选择尽可能未知的节点探索\n",
    "            nodes_to_select = list(set(nodes).difference(set(known_chains.keys()).union(set(known_nodes))))\n",
    "            selected_node = nodes_to_select[random.randint(0, len(nodes_to_select)-1)]\n",
    "            for node in nodes_to_select:\n",
    "                if len(depths[node]) > len(depths[selected_node]):\n",
    "                    selected_node = node\n",
    "            known_chains[selected_node] = test_input[selected_node]\n",
    "            update(known_chains, nodes, depths, parents)\n",
    "            for node in nodes:\n",
    "                if len(depths[node]) == 1:\n",
    "                    known_nodes.add(node)\n",
    "            if len(nodes) - 1 == len([i for i in parents.keys() if parents[i] is not None]):\n",
    "                break\n",
    "    data = data._append({\"num_nodes\": len(nodes), \"method\": \"1\", \"explore_times\": len(nodes)}, ignore_index=True)\n",
    "    data = data._append({\"num_nodes\": len(nodes), \"method\": \"2\", \"explore_times\": explore_times/repeat_times}, ignore_index=True)\n",
    "    data = data._append({\"num_nodes\": len(nodes), \"method\": \"3\", \"explore_times\": explore_times_2/repeat_times}, ignore_index=True)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_nodes</th>\n",
       "      <th>method</th>\n",
       "      <th>explore_times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>13.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1495</th>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>28.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1496</th>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>28.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1497</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     num_nodes method explore_times\n",
       "0            2      1             2\n",
       "1            2      2           1.8\n",
       "2            2      3           2.0\n",
       "3           17      1            17\n",
       "4           17      2          13.8\n",
       "...        ...    ...           ...\n",
       "1495        37      2          28.8\n",
       "1496        37      3          28.2\n",
       "1497        17      1            17\n",
       "1498        17      2          14.0\n",
       "1499        17      3          13.6\n",
       "\n",
       "[1500 rows x 3 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "methods = [\"1\", \"2\"]\n",
    "x = sorted(list(set(data[\"num_nodes\"])))\n",
    "method_to_lagend={\n",
    "    \"1\": \"未使用拓扑推理算法\",\n",
    "    \"2\": \"使用拓扑推理算法\"\n",
    "}\n",
    "ys = {}\n",
    "for method in methods:\n",
    "    y = []\n",
    "    for num_nodes in x: \n",
    "        tmp = list(data[(data[\"num_nodes\"]==num_nodes) & (data[\"method\"] == method)][\"explore_times\"])\n",
    "        y.append(sum(tmp) / len(tmp))\n",
    "    ys[method] = y\n",
    "\n",
    "for method in methods:\n",
    "    plt.plot(x, ys[method], label=method_to_lagend[method])\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title(\"拓扑推理算法的效率验证\")\n",
    "plt.xlabel(\"节点数量\")\n",
    "plt.ylabel(\"拓扑识别所需探索节点的平均数量\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.2255335244225258"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "methods = [\"1\", \"2\"]\n",
    "x = sorted(list(set(data[\"num_nodes\"])))\n",
    "y = []\n",
    "method_to_lagend={\n",
    "    \"1\": \"未使用拓扑推理算法\",\n",
    "    \"2\": \"使用拓扑推理算法\"\n",
    "}\n",
    "\n",
    "for i in range(len(x)): \n",
    "    y.append((ys[\"1\"][i]-ys[\"2\"][i])/ys[\"1\"][i])\n",
    "\n",
    "\n",
    "plt.plot(x, y)\n",
    "\n",
    "# plt.legend()\n",
    "\n",
    "plt.title(\"拓扑推理算法的效率验证\")\n",
    "plt.xlabel(\"节点数量\")\n",
    "plt.ylabel(\"拓扑推理算法所带来的效率提升比例\")\n",
    "plt.show()\n",
    "\n",
    "sum(y) / len(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "org: 17---now: 0.69---improve ratio: 0.9594117647058824\n"
     ]
    }
   ],
   "source": [
    "times = 0\n",
    "for _ in range(50):\n",
    "    known_chains = {}\n",
    "    known_nodes = set()\n",
    "    depths = {i: [d + 1 for d in range(len(nodes))] for i in nodes}\n",
    "    parents = {i: None for i in nodes}\n",
    "    for _ in range(len(nodes)):\n",
    "        times += 1\n",
    "        # # way 1: 纯推理，无探索策略（废弃）\n",
    "        # nodes_to_select = list(set(nodes).difference(set(known_chains.keys())))\n",
    "        # selected_node = nodes_to_select[random.randint(0, len(nodes_to_select)-1)]\n",
    "        # way 2: 推理+不探索已知节点\n",
    "        nodes_to_select = list(set(nodes).difference(set(known_chains.keys()).union(set(known_nodes))))\n",
    "        selected_node = nodes_to_select[random.randint(0, len(nodes_to_select)-1)]\n",
    "        # # way 3: 推理+不探索已知节点+选择尽可能未知的节点探索\n",
    "        # nodes_to_select = list(set(nodes).difference(set(known_chains.keys()).union(set(known_nodes))))\n",
    "        # selected_node = nodes_to_select[random.randint(0, len(nodes_to_select)-1)]\n",
    "        # for node in nodes_to_select:\n",
    "        #     if len(depths[node]) > len(depths[selected_node]):\n",
    "        #         selected_node = node\n",
    "\n",
    "        known_chains[selected_node] = test_input[selected_node]\n",
    "        update(known_chains, nodes, depths, parents)\n",
    "        for node in nodes:\n",
    "            if len(depths[node]) == 1:\n",
    "                known_nodes.add(node)\n",
    "        if len(nodes) - 1 == len([i for i in parents.keys() if parents[i] is not None]):\n",
    "            break\n",
    "\n",
    "print(f\"org: {len(nodes)}---now: {times/1000}---improve ratio: {1-times/1000/len(nodes)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "└── 2\n",
      "    ├── 3\n",
      "    │   ├── 6\n",
      "    │   ├── 7\n",
      "    │   ├── 8\n",
      "    │   ├── 9\n",
      "    │   └── 10\n",
      "    ├── 4\n",
      "    │   ├── 11\n",
      "    │   ├── 12\n",
      "    │   ├── 13\n",
      "    │   ├── 14\n",
      "    │   └── 15\n",
      "    └── 5\n",
      "        ├── 16\n",
      "        └── 17\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 如果树全部已知，则打印\n",
    "if len(nodes) - 1 == len([i for i in parents.keys() if parents[i] is not None]):\n",
    "    from treelib import Node, Tree\n",
    "\n",
    "    tree = Tree()\n",
    "\n",
    "    for depth in range(max(v[0] for v in depths.values())):\n",
    "        for node in nodes:\n",
    "            if depths[node][0] == depth + 1:\n",
    "                tree.create_node(node, node, parents[node])\n",
    "            \n",
    "    print(tree)"
   ]
  }
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